Nonlinear system identification using a cuckoo search optimized adaptive Hammerstein model

A novel nonlinear system identification scheme is proposed.A Hammerstein model has been trained using cuckoo search algorithm.The model is a cascade of a FLANN and an adaptive IIR filter.Simulation study shows enhanced modeling capacity of the proposed scheme.The new schemes offers lesser computational time over other methods studied. An attempt has been made in this paper to model a nonlinear system using a Hammerstein model. The Hammerstein model considered in this paper is a functional link artificial neural network (FLANN) in cascade with an adaptive infinite impulse response (IIR) filter. In order to avoid local optima issues caused by conventional gradient descent training strategies, the model has been trained using a cuckoo search algorithm (CSA), which is a recently proposed stochastic algorithm. Modeling accuracy of the proposed scheme has been compared with that obtained using other popular evolutionary computing algorithms for the Hammerstein model. Enhanced modeling capability of the CSA based scheme is evident from the simulation results.

[1]  Goutam Chakraborty,et al.  Legendre-FLANN-based nonlinear channel equalization in wireless communication system , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.

[2]  A. C. Tsoi,et al.  Nonlinear system identification using multilayer perceptrons with locally recurrent synaptic structure , 1992, Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop.

[3]  Ganapati Panda,et al.  Identification of nonlinear dynamic systems using functional link artificial neural networks , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[4]  Hong Gu,et al.  Parameter Identification of Bilinear System Based on Genetic Algorithm , 2007, LSMS.

[5]  Ganapati Panda,et al.  Distributed and robust parameter estimation of IIR systems using incremental particle swarm optimization , 2013, Digit. Signal Process..

[6]  Hamid Khaloozadeh,et al.  Nonlinear Hammerstein model identification using genetic algorithm , 2002, Proceedings 2002 IEEE International Conference on Artificial Intelligence Systems (ICAIS 2002).

[7]  Yin Ming-hao,et al.  Parameter estimation for chaotic systems using the cuckoo search algorithm with an orthogonal learning method , 2012 .

[8]  Ganapati Panda,et al.  Improved identification of Hammerstein plants using new CPSO and IPSO algorithms , 2010, Expert Syst. Appl..

[9]  Xin-She Yang,et al.  Cuckoo search: recent advances and applications , 2013, Neural Computing and Applications.

[10]  Goutam Chakraborty,et al.  Nonlinear channel equalization for wireless communication systems using Legendre neural networks , 2009, Signal Process..

[11]  Hamidreza Modares,et al.  Parameter estimation of bilinear systems based on an adaptive particle swarm optimization , 2010, Eng. Appl. Artif. Intell..

[12]  Nithin V. George,et al.  Convex combination of nonlinear adaptive filters for active noise control , 2014 .

[13]  S. Thomas Alexander,et al.  Adaptive Signal Processing , 1986, Texts and Monographs in Computer Science.

[14]  Alex ChiChung Kot,et al.  Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[15]  Ashish Kumar Bhandari,et al.  Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur's entropy , 2014, Expert Syst. Appl..

[16]  Xin-She Yang,et al.  Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan , 2014, Appl. Soft Comput..

[17]  Goutam Chakraborty,et al.  Development of Laguerre Neural-Network-Based Intelligent Sensors for Wireless Sensor Networks , 2011, IEEE Transactions on Instrumentation and Measurement.

[18]  H.-X. Li,et al.  Identification of Hammerstein models using genetic algorithms , 1999 .

[19]  Ling Wang,et al.  Parameter estimation for chaotic systems by particle swarm optimization , 2007 .

[20]  M. Nazmul Karim,et al.  A New Method for the Identification of Hammerstein Model , 1997, Autom..

[21]  Stanley H. Johnson,et al.  Use of Hammerstein Models in Identification of Nonlinear Systems , 1991 .

[22]  Ganapati Panda,et al.  Development and performance evaluation of FLANN based model for forecasting of stock markets , 2009, Expert Syst. Appl..

[23]  K. Narendra,et al.  An iterative method for the identification of nonlinear systems using a Hammerstein model , 1966 .

[24]  Pinar Civicioglu,et al.  A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms , 2013, Artificial Intelligence Review.

[25]  Wei-Der Chang.  Coefficient estimation of IIR filter by a multiple crossover genetic algorithm , 2006, Comput. Math. Appl..

[26]  Gurjit Singh Walia,et al.  Intelligent video target tracking using an evolutionary particle filter based upon improved cuckoo search , 2014, Expert Syst. Appl..

[27]  Li Xiangyang,et al.  A novel self-optimizing approach for knowledge acquisition , 2000 .

[28]  Weerakorn Ongsakul,et al.  Cuckoo search algorithm for non-convex economic dispatch , 2013 .

[29]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[30]  T. Hachino,et al.  Identification of Hammerstein model using radial basis function networks and genetic algorithm , 2004, 2004 5th Asian Control Conference (IEEE Cat. No.04EX904).

[31]  Nithin V. George,et al.  On a cuckoo search optimization approach towards feedback system identification , 2014, Digit. Signal Process..